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Aerodynamic effects on outer ash deposition rates in second generation atmospheric pressure oxy-coal combustion systems
Recent measurements in scenarios representative of second generation atmospheric pressure oxy-coal combustion systems have shown a significant increase in ash deposition rates in comparison to combustion in air. However, the causative mechanisms behind this increase have not been well understood. To fill this void, well-characterized experiments including fuel and deposit particle size distributions (PSDs) were coupled with highly resolved numerical simulations to isolate the aerodynamic effects impacting the deposition process such that mechanisms could be hypothesized. Three combustion scenarios (AIR, OXY27, OXY70) spanning a factor of three variation in flue gas volumetric flow rates were simulated and the deposition characteristics (impaction rates, deposit PSD, temperature, residence times, capture rates) tracked/predicted using a customized deposition module. The measured deposit PSD was significantly different from the PSD of the parent fuel indicating significant physio-chemical transformations (coalescence and particle growth in particular) at play. The use of simplistic modeling approaches (swelling parameter variations) to model particle growth led to inaccurate deposit ash PSD and rate predictions. A more satisfactory agreement between the measurements and simulations was obtained when the functional form of the parent fuel PSD (spread parameter) was modified to conform closely to the deposit PSD while still ensuring the fidelity of temperature and velocity predictions. Based on the measured ash compositions, identical capture criteria were employed across all three scenarios. Here this study further supports the theory that the ash deposition rates in these systems are dominated by aerodynamic effects with the ash PSD playing a dominant role.
Measurements and Prediction of Ash Deposition in a Cyclone-Fired Boiler Operating under Variable Load Conditions
Measurements of ash deposition rates were made between the secondary superheater and reheater sections of a 450 MW cyclone-fired lignite boiler as the operational load varied from 33 to 100%. Significant reductions in deposition rates with a decrease in operational load were observed. To uncover the causative mechanisms behind these observations, operational data from the power plant were used to carry out computational fluid dynamic (CFD) simulations of the boiler. After ascertaining that the gas temperatures and velocities at various sections within the boiler were being represented adequately, decoupled simulations of the ash deposition process on the deposit probe were carried out using a finely resolved boundary layer mesh. Fly ash particle size distribution (PSD) and its concentration for the decoupled calculations were determined from stand-alone cyclone barrel simulations. The ash partitioning (mass %) between the fly ash and slag was found to be ~50:50, which was in line with previous field observations, and it did not vary significantly across different cyclone loads. The predicted PSD of the deposit ash was concentrated in the size range 10–30 microns, which was in agreement with cross-sectional images of the deposit obtained from the measurements. At lower loads, sharp variations in the deposition rates were predicted in the gas temperature range 950–1150 K. The particle kinetic energy—particle viscosity-based capture methodology utilized in this study in conjunction with appropriate ash compositions, ash viscosity models and gas temperature estimates can help estimate slagging propensities at different loads reasonably well in these systems.
Modeling ash deposition and shedding during oxy-combustion of coal/rice husk blends at 70% inlet O2
Abstract Co-firing rice husk (RH) and coal with carbon capture using oxy-combustion presents a net carbon negative energy production opportunity. In addition, the high fusion temperature of the non-sticky, silica rich, RH can mitigate ash deposition as well as promote shedding of deposits. To identify the optimum operating conditions, fuel particle sizes, and blend ratios that minimize ash deposition, a Computational Fluid Dynamic methodology with add-on ash deposition and shedding models were employed to predict outer ash deposition and shedding rates during co-combustion of coal/RH in AIR and O 2 /CO 2 (70/30 vol%, OXY70) oxidizer compositions. After ensuring that the fly-ash particle size distributions and particle Stokes numbers near the deposition surface were accurately represented (to model impaction), appropriate models for coal ash and RH ash viscosities that were accurate in the temperature region (1200–1300 K) of interest in this study were identified. A particle viscosity and kinetic energy (PKE) based capture criterion was enforced to model the ash capture. An erosion/shedding criterion that takes the deposit melt fraction and the energy consumed during particle impact into account was also implemented. Deposition rate predictions as well as the deposition rate enhancement (OXY70/AIR) were in good agreement with measured values. While the OXY70 scenario was associated with a significant reduction (60%–70%) in flue gas velocities, it also resulted in larger fly-ash particles. As a result, the PKE distributions of the erosive RH ash were similar in both scenarios and resulted in similar shedding rates.
An Integrated Approach to Predicting Ash Deposition and Heat Transfer in Coal-Fired Boilers
The overall goal of this project is to develop via measurements and simulations an advanced online technology to predict, monitor and manage fireside ash deposition in a coal-fired boiler allowing for more efficient operations under a range of load conditions and fuel property variability. With this in place fuel sorting and blending can be done upstream and operations can be optimized to compensate for load and fuel properties. In support of this objective, three experimental campaigns were undertaken during the course of the project to measure ash deposition rates within the boiler at different fuel flow rates and its ash composition variability. Simulations of the experimental conditions representing actual geometry, operational scenarios in terms of air flow rates, coal flow rates as well as coal compositions, heating values, and particle size distributions were also carried out. Deposition rates were predicted using a unique particle kinetic energy and viscosity based ash deposition methodology whose validity was ascertained by comparing against deposition rate measurements for widely varying operating conditions and ash compositions in a lab-scale furnace. With a unique end-to-end combustion modeling methodology established and different simulation scenarios carried out, the results from our computational fluid dynamic (CFD) simulations in conjunction with the plant data summarized in this report were used to refine Microbeam Technology Incorporated’s MTI CSPI-CT Tool to predict and monitor fire-side ash deposition under a range of load conditions and fuel property variability in real time.
Particle Size Distribution and Its Impacts on Ash Deposition and Radiative Transfer during Oxy-Combustion of Rice Husk–Natural Gas
Rice husk (RH) co-combustion with natural gas in highly oxygen-enriched concentrations presents a net carbon-negative energy production opportunity while minimizing flue gas recycling. However, recent experiments have shown enhanced ash deposition rates in oxygen-enriched conditions, with deposition/shedding also being dependent on the particle size distribution (PSD) of the parent RH fuel. To uncover the causative mechanisms behind these observations, add-on models for ash deposition/shedding and radiative properties were employed in computational fluid dynamics simulations. The combustion scenarios investigated encompassed two types of RH (US RH, Chinese RH) with widely varying ash contents (by % mass) and inlet fuel PSD with air and O2/CO2 (70/30 vol %, OXY70) as oxidizers. Utilizing the measured fly-ash PSDs near the deposit surface and modeling the particle viscosity accurately, particle kinetic-energy (PKE)-based capture and shedding criteria were identified as the keys to accurate deposition/shedding rate predictions. The OXY70 scenarios showed higher ash-capturing propensities due to their lower PKE. Conversely, higher erosion rates were predicted in the AIR firing scenarios. In addition, the radiative characteristics across all the scenarios were dominated by the gases and were not sensitive to the fly-ash PSD. Therefore, the higher particle concentrations in the OXY70 conditions did not negatively impact the heat extraction.
Update on Mitigation of Aerosol Impacts on Ash Deposition and Emission from Coal Combustion
Barr, in partnership with the University of North Dakota, Microbeam Technologies Inc., Envergex LLC, and MLJ Consulting, worked to develop a transformational technology that controls the formation of alkali aerosols found in coal ash. This technology mitigates ash deposition by injecting sorbents into the boiler and capturing volatile species responsible for boiler fouling, the primary cause for boiler outages. In this session, we’ll explore the science behind this technology and see some real-world results of its implementation.
Mitigation of Aerosol Impacts of Ash Deposition and Emissions from Coal Combustion (Final Technical Report)
This project was selected under FOA-0001989 Area of Interest (AOI) 2 – Power Plant Component Improvement. The objective of this AOI was to test and validate technologies and methodologies that can improve the efficiency, reliability, and/or flexibility of individual power plant components in existing plants. The aerosol mitigation technology comprises injection of sorbent into the boiler through injection lances using a positive-pressure blower. The injected sorbent interacts with flame-vaporized aerosolforming components produced during combustion reducing the abundance of sub-micrometer (< 1 micron) particles and fireside ash deposits in the boiler. The primary objective of this project was to validate a sorbent-based technology for mitigating fireside heat transfer surface fouling-related challenges in a full-scale coal-fired power generation facility, thereby, improving plant performance and reliability.
Aerodynamic influences on the outer ash deposition rates during oxy-coal combustion
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Enrichment mechanism of arsenic in fine ash deposits during co-combustion of rice husk and coal
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Modelling Outer Ash Deposition Rates in Second Generation Atmospheric Pressure Oxy-Fuel Combustion Systems
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Large-eddy simulation of coal combustion in 15 MW pilot-scale boiler-simulation facility
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Gaussian Process Emulators for Volcanic Ash Dispersion Model Tephra2
It is necessary to predict volcanic ash deposition since falling ash is harmful to human activities. Because simulators built for this purpose are computationally expensive, it is popular to use statistical emulators for geophysical hazard analyses, where a large number of simulations are required. Gaussian stochastic process emulators are able to approximate expensive simulations in an accurate and efficient way. Using a relatively small number of simulation runs, a well-trained emulator can accurately predict simulation outputs at massive new input points in a few seconds. Under different explosive eruption conditions, we constructed Gaussian stochastic process emulators for Tephra2, a simulation tool for estimating the accumulation of volcanic ash over a region. Historical wind records are used as input wind data, without assuming a Gaussian wind speed profile or common wind direction among elevations. While there are several inputs for Tephra2, we use no more than three physically motivated variables as emulator inputs to reduce the computational cost of emulations. The emulator outputs predict the mass of tephra per unit area at 50281 grid points around a predetermined vent location.
Impact of Pycnonuclear Fusion Uncertainties on the Cooling of Accreting Neutron Star Crusts
Abstract The observation of X-rays during quiescence from transiently accreting neutron stars provides unique clues about the nature of dense matter. This, however, requires extensive modeling of the crusts and matching the results to observations. The pycnonuclear fusion reaction rates implemented in these models are theoretically calculated by extending phenomenological expressions and have large uncertainties spanning many orders of magnitude. We present the first sensitivity studies of these pycnonuclear fusion reactions in realistic network calculations. We also couple the reaction network with the thermal evolution code dStar to further study their impact on the neutron star cooling curves in quiescence. Varying the pycnonuclear fusion reaction rates alters the depth at which nuclear heat is deposited although the total heating remains constant. The enhancement of the pycnonuclear fusion reaction rates leads to an overall shallower deposition of nuclear heat. The impurity factors are also altered depending on the type of ashes deposited on the crust. These total changes correspond to a variation of up to 9 eV in the modeled cooling curves. While this is not sufficient to explain the shallow heat source, it is comparable to the observational uncertainties and can still be important for modeling the neutron star crust.
Tetrafluoroboric Acid Digestion for Accurate Determination of Rare Earth Elements in Coal and Fly Ash by ICP-MS Analysis
Coal and coal-related fly ash often contain rare earth elements (REEs) that have the potential to be utilized as valuable mineral resources. Accurately determining the REE content in coal and fly ash is crucial for resource evaluation. The conventional approach involves using hydrofluoric acid (HF) to dissolve silicates and release REEs, which, however, prolongs the digestion process due to the additional step of complexing fluoride ions (F−) with boric acid (H3BO3). Determining the correct amount of H3BO3 for neutralization can be challenging, and in some instances, the binding of fluoride ions with certain lanthanides (Lns) hampers the accurate determination of all 14 naturally occurring rare earth elements in a single digestion batch by inductively coupled plasma mass spectrometry (ICP-MS). In this study, we present an alternative method that achieves the accurate determination of all 14 naturally occurring REEs using tetrafluoroboric acid (HBF4) followed by ICP-MS analysis. This approach eliminates the need for an F− complexing step. We tested this method on certified REE reference materials, including NIST 1632e (coal) and NIST 1633c (fly ash), as well as the REE geological reference material USGS AGV-1 (andesite). Our results demonstrated excellent recovery rates (relative standard deviation, RSD < ±10%), with a correlation coefficient (r2) exceeding 0.99. Using this method, we investigated the concentrations of all 14 REEs in coal and fly ash samples collected from various locations in the southwestern USA. This improved digestion technique streamlines the analysis process and enhances the accuracy of REE determination, facilitating a more comprehensive evaluation of REE-rich coal and fly ash deposits for resource exploration.
Potential for constraining sequence stratigraphy and cycle stratigraphy with U-Pb dating of carbonates
Studies of sequence stratigraphy and cycle stratigraphy in carbonate formations increasingly improve our understanding of depositional systems and basinal to global correlations. However, there is still a scarcity of dated sequences to determine the time length of sequences and cycles. Furthermore, the durations of sequences and cycles provide tests of the processes that produce them. Poor age resolution mostly stems from the lack of recognizable volcanic ash deposits in many sections. Through continuing advances in analytical techniques and approaches to sampling, the current state of Usingle bondPb dating of carbonate sedimentary rocks provides opportunities to fill in age gaps. The approach should build on well-constrained 3-dimensional stratigraphic architectures and diagenetic stratigraphies.
Predicting wind-driven spatial deposition through simulated color images using deep autoencoders
Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.
Characteristics of the sub-micron ash aerosol generated during oxy-coal combustion at atmospheric and elevated pressures
This paper is concerned with the effect of pressure on the particle size distribution and the size-segregated composition of the sub-micron ash aerosol created during oxy-coal combustion under near practical self-sustaining combustion conditions. The problem is important because pressurized oxy-coal combustion has been proposed as one promising technology to minimize CO 2 emissions. Sub-micron ash plays a major role in ash deposition mechanisms, which, in turn, can control boiler performance. In this work, the same bituminous coal was burned at pressures of 1, 8 and 15 bar in O 2 /CO 2 environments. Here, tests employed a 100 kW (rated) oxy-fuel combustor (OFC) operated at atmospheric pressure (1 bar) and a 300 kW (rated) entrained-flow pressurized reactor (EFPR) at elevated pressures (8 and 15 bar). Although these tests were conducted under near practical combustion conditions, confounding effects of peak flame temperature variations were minimized for the 1 bar and 15 bar tests, allowing the role of elevated pressure to be isolated. For the EFPR tests, a specially designed sampling system was used to sample sub-micron ash aerosols from the pressurized combustor and is described in detail. Results showed that at the same peak temperature but higher pressure, the fractions of ash aerosol partitioned into the PM 0.6 and PM 1 size fractions were greatly diminished. Moreover, elevated pressures caused significant changes in the composition of the (size-segregated) sub-micron aerosol, especially in its alkali content, which increased significantly. Examination of fractions of each element that ended up in the sub-micron fume suggested that, at constant temperature, the effect of pressure on vaporization of semi-volatile metals was very different from that on the release into the gas phase of non-volatile metals and could not be explained by equilibrium.